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While computer scientists have been advertising Artificial Intelligence for more than half a century, the technology is just beginning to uncover its true potential. Despite all the hype, machine learning, deep learning, computer vision and natural language processing have, silently, become entrenched in many people’s daily routines. These innovations have brought with them new abilities …

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For big companies, success is a blessing and a curse. You don’t get big without doing something (or many things) very right. It might start with an invention or service the world didn’t know it needed. Your product takes off, and growth brings a whole new set of logistical challenges. Delivering consistent quality, hiring the right team, establishing a strong culture, tapping into new markets, satisfying shareholders. The list goes on.

Eventually, however, what made you successful also makes you resistant to change.

You’ve built a machine for one purpose, and it’s running smoothly, but what about retooling that machine to make something new? Not so easy. Leaders of big companies know there is no future for their organizations without change. And yet, they struggle to drive it.

In their new book, Leading Transformation: How to Take Charge of Your Company’s Future, Kyle Nel, Nathan Furr, and Thomas Ramsøy aim to deliver a roadmap for corporate transformation.

The book focuses on practical tools that have worked in big companies to break down behavioral and cognitive biases, envision radical futures, and run experiments. These include using science fiction and narrative to see ahead and adopting better measures of success for new endeavors.

A thread throughout is how to envision a new future and move into that future.

We’re limited by the bubbles in which we spend the most time—the corporate bubble, the startup bubble, the nonprofit bubble. The mutually beneficial convergence of complementary bubbles, then, can be a powerful tool for kickstarting transformation. The views and experiences of one partner can challenge the accepted wisdom of the other; resources can flow into newly co-created visions and projects; and connections can be made that wouldn’t otherwise exist.

The authors call such alliances uncommon partners. In the following excerpt from the book, Made In Space, a startup building 3D printers for space, helps Lowe’s explore an in-store 3D printing system, and Lowe’s helps Made In Space expand its vision and focus.

Uncommon Partners
In a dingy conference room at NASA, five prototypical nerds, smelling of Thai food, laid out the path to printing satellites in space and buildings on distant planets. At the end of their four-day marathon, they emerged with an artifact trail that began with early prototypes for the first 3D printer on the International Space Station and ended in the additive-manufacturing future—a future much bigger than 3D printing.

In the additive-manufacturing future, we will view everything as transient, or capable of being repurposed into new things. Rather than throwing away a soda bottle or a bent nail, we will simply reprocess these things into a new hinge for the fence we are building or a light switch plate for the tool shed. Indeed, we might not even go buy bricks for the tool shed, but instead might print them from impurities pulled from the air and the dirt beneath our feet. Such a process would both capture carbon in the air to make the bricks and avoid all the carbon involved in making and then transporting traditional bricks to your house.

If it all sounds a little too science fiction, think again. Lowe’s has already been honored as a Champion of Change by the US government for its prototype system to recycle plastic (e.g., plastic bags and bottles). The future may be closer than you have imagined. But to get there, Lowe’s didn’t work alone. It had to work with uncommon partners to create the future.

Uncommon partners are the types of organizations you might not normally work with, but which can greatly help you create radical new futures. Increasingly, as new technologies emerge and old industries converge, companies are finding that working independently to create all the necessary capabilities to enter new industries or create new technologies is costly, risky, and even counterproductive. Instead, organizations are finding that they need to collaborate with uncommon partners as an ecosystem to cocreate the future together. Nathan [Furr] and his colleague at INSEAD, Andrew Shipilov, call this arrangement an adaptive ecosystem strategy and described how companies such as Lowe’s, Samsung, Mastercard, and others are learning to work differently with partners and to work with different kinds of partners to more effectively discover new opportunities. For Lowe’s, an adaptive ecosystem strategy working with uncommon partners forms the foundation of capturing new opportunities and transforming the company. Despite its increased agility, Lowe’s can’t be (and shouldn’t become) an independent additive-manufacturing, robotics-using, exosuit-building, AR-promoting, fill-in-the-blank-what’s-next-ing company in addition to being a home improvement company. Instead, Lowe’s applies an adaptive ecosystem strategy to find the uncommon partners with which it can collaborate in new territory.

To apply the adaptive ecosystem strategy with uncommon partners, start by identifying the technical or operational components required for a particular focus area (e.g., exosuits) and then sort these components into three groups. First, there are the components that are emerging organically without any assistance from the orchestrator—the leader who tries to bring together the adaptive ecosystem. Second, there are the elements that might emerge, with encouragement and support. Third are the elements that won’t happen unless you do something about it. In an adaptive ecosystem strategy, you can create regular partnerships for the first two elements—those already emerging or that might emerge—if needed. But you have to create the elements in the final category (those that won’t emerge) either with an uncommon partner or by yourself.

For example, when Lowe’s wanted to explore the additive-manufacturing space, it began a search for an uncommon partner to provide the missing but needed capabilities. Unfortunately, initial discussions with major 3D printing companies proved disappointing. The major manufacturers kept trying to sell Lowe’s 3D printers. But the vision our group had created with science fiction was not for vendors to sell Lowe’s a printer, but for partners to help the company build a system—something that would allow customers to scan, manipulate, print, and eventually recycle additive-manufacturing objects. Every time we discussed 3D printing systems with these major companies, they responded that they could do it and then tried to sell printers. When Carin Watson, one of the leading lights at Singularity University, introduced us to Made In Space (a company being incubated in Singularity University’s futuristic accelerator), we discovered an uncommon partner that understood what it meant to cocreate a system.

Initially, Made In Space had been focused on simply getting 3D printing to work in space, where you can’t rely on gravity, you can’t send up a technician if the machine breaks, and you can’t release noxious fumes into cramped spacecraft quarters. But after the four days in the conference room going over the comic for additive manufacturing, Made In Space and Lowe’s emerged with a bigger vision. The company helped lay out an artifact trail that included not only the first printer on the International Space Station but also printing system services in Lowe’s stores.

Of course, the vision for an additive-manufacturing future didn’t end there. It also reshaped Made In Space’s trajectory, encouraging the startup, during those four days in a NASA conference room, to design a bolder future. Today, some of its bold projects include the Archinaut, a system that enables satellites to build themselves while in space, a direction that emerged partly from the science fiction narrative we created around additive manufacturing.

In summary, uncommon partners help you succeed by providing you with the capabilities you shouldn’t be building yourself, as well as with fresh insights. You also help uncommon partners succeed by creating new opportunities from which they can prosper.

Helping Uncommon Partners Prosper
Working most effectively with uncommon partners can require a shift from more familiar outsourcing or partnership relationships. When working with uncommon partners, you are trying to cocreate the future, which entails a great deal more uncertainty. Because you can’t specify outcomes precisely, agreements are typically less formal than in other types of relationships, and they operate under the provisions of shared vision and trust more than binding agreement clauses. Moreover, your goal isn’t to extract all the value from the relationship. Rather, you need to find a way to share the value.

Ideally, your uncommon partners should be transformed for the better by the work you do. For example, Lowe’s uncommon partner developing the robotics narrative was a small startup called Fellow Robots. Through their work with Lowe’s, Fellow Robots transformed from a small team focused on a narrow application of robotics (which was arguably the wrong problem) to a growing company developing a very different and valuable set of capabilities: putting cutting-edge technology on top of the old legacy systems embedded at the core of most companies. Working with Lowe’s allowed Fellow Robots to discover new opportunities, and today Fellow Robots works with retailers around the world, including BevMo! and Yamada. Ultimately, working with uncommon partners should be transformative for both of you, so focus more on creating a bigger pie than on how you are going to slice up a smaller pie.

The above excerpt appears in the new book Leading Transformation: How to Take Charge of Your Company’s Future by Kyle Nel, Nathan Furr, and Thomas Ramsøy, published by Harvard Business Review Press.

Image Credit: Here / Shutterstock.com

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While computer scientists have been advertising Artificial Intelligence for more than half a century, the technology is just beginning to uncover its true potential. Despite all the hype, machine learning, deep learning, computer vision and natural language processing have, silently, become entrenched in many people’s daily routines. These innovations have brought with them new abilities …

The post What is Machine Learning Going to Do – Artificial Intelligence is Here in Digital Marketing! appeared first on TFOT. Continue reading →

Autonomous vehicles can follow the general rules of American roads, recognizing traffic signals and lane markings, noticing crosswalks and other regular features of the streets. But they work only on well-marked roads that are carefully scanned and mapped in advance.

Many paved roads, though, have faded paint, signs obscured behind trees and unusual intersections. In addition, 1.4 million miles of U.S. roads—one-third of the country’s public roadways—are unpaved, with no on-road signals like lane markings or stop-here lines. That doesn’t include miles of private roads, unpaved driveways or off-road trails.

What’s a rule-following autonomous car to do when the rules are unclear or nonexistent? And what are its passengers to do when they discover their vehicle can’t get them where they’re going?

Accounting for the Obscure
Most challenges in developing advanced technologies involve handling infrequent or uncommon situations, or events that require performance beyond a system’s normal capabilities. That’s definitely true for autonomous vehicles. Some on-road examples might be navigating construction zones, encountering a horse and buggy, or seeing graffiti that looks like a stop sign. Off-road, the possibilities include the full variety of the natural world, such as trees down over the road, flooding and large puddles—or even animals blocking the way.

At Mississippi State University’s Center for Advanced Vehicular Systems, we have taken up the challenge of training algorithms to respond to circumstances that almost never happen, are difficult to predict and are complex to create. We seek to put autonomous cars in the hardest possible scenario: driving in an area the car has no prior knowledge of, with no reliable infrastructure like road paint and traffic signs, and in an unknown environment where it’s just as likely to see a cactus as a polar bear.

Our work combines virtual technology and the real world. We create advanced simulations of lifelike outdoor scenes, which we use to train artificial intelligence algorithms to take a camera feed and classify what it sees, labeling trees, sky, open paths and potential obstacles. Then we transfer those algorithms to a purpose-built all-wheel-drive test vehicle and send it out on our dedicated off-road test track, where we can see how our algorithms work and collect more data to feed into our simulations.

Starting Virtual
We have developed a simulator that can create a wide range of realistic outdoor scenes for vehicles to navigate through. The system generates a range of landscapes of different climates, like forests and deserts, and can show how plants, shrubs and trees grow over time. It can also simulate weather changes, sunlight and moonlight, and the accurate locations of 9,000 stars.

The system also simulates the readings of sensors commonly used in autonomous vehicles, such as lidar and cameras. Those virtual sensors collect data that feeds into neural networks as valuable training data.

Simulated desert, meadow and forest environments generated by the Mississippi State University Autonomous Vehicle Simulator. Chris Goodin, Mississippi State University, Author provided.
Building a Test Track
Simulations are only as good as their portrayals of the real world. Mississippi State University has purchased 50 acres of land on which we are developing a test track for off-road autonomous vehicles. The property is excellent for off-road testing, with unusually steep grades for our area of Mississippi—up to 60 percent inclines—and a very diverse population of plants.

We have selected certain natural features of this land that we expect will be particularly challenging for self-driving vehicles, and replicated them exactly in our simulator. That allows us to directly compare results from the simulation and real-life attempts to navigate the actual land. Eventually, we’ll create similar real and virtual pairings of other types of landscapes to improve our vehicle’s capabilities.

A road washout, as seen in real life, left, and in simulation. Chris Goodin, Mississippi State University, Author provided.
Collecting More Data
We have also built a test vehicle, called the Halo Project, which has an electric motor and sensors and computers that can navigate various off-road environments. The Halo Project car has additional sensors to collect detailed data about its actual surroundings, which can help us build virtual environments to run new tests in.

The Halo Project car can collect data about driving and navigating in rugged terrain. Beth Newman Wynn, Mississippi State University, Author provided.
Two of its lidar sensors, for example, are mounted at intersecting angles on the front of the car so their beams sweep across the approaching ground. Together, they can provide information on how rough or smooth the surface is, as well as capturing readings from grass and other plants and items on the ground.

Lidar beams intersect, scanning the ground in front of the vehicle. Chris Goodin, Mississippi State University, Author provided
We’ve seen some exciting early results from our research. For example, we have shown promising preliminary results that machine learning algorithms trained on simulated environments can be useful in the real world. As with most autonomous vehicle research, there is still a long way to go, but our hope is that the technologies we’re developing for extreme cases will also help make autonomous vehicles more functional on today’s roads.

If you have seen science fiction television series such as Humans or Westworld, you might be imagining a near future where intelligent, humanoid robots play an important role in meeting the needs of people, including caring for children or older relatives. Continue reading →